I write this letter to shed light on the data science industry as much as I can. This may help some of you find answers to questions like “Can I switch to this industry if I have no math background?”, “Do I need to be innovative to solve an industry problem?”, or “Do my current skill sets add value to a data science project?”. I shared my story to let you know where I come from along with the status quo of the industry to understand how you should decide. Please note that I tried to be neutral on the status quo.
My journey into data science started in 2005 when I became familiar with a beautiful concept named manifold learning. I loved mathematics so I fell in love with manifold learning. You had to be good at linear algebra, analytic geometry, and probability theory to understand and implement manifold learning techniques. I developed a face recognition engine using these techniques that worked effectively, at least as a student project. Using manifold learning techniques, I was able to interpret high-dimensional data which was very hard to analyze. That was giving me power.
Today, the industry does not ask you to know the math behind the algorithms. Why? Because most of the solutions are built through data-focused, rather than model-focused, methodologies . So, it would be ok if you do not know math in depth. You just need to learn how to use libraries.
In 2013, I started learning natural language processing or NLP with a powerful library named NLTK. In that year, I had a chance to be part of an innovative company working on a gesture control armband. The company aimed to build an armband that can fit on your forearm and recognize your hand gestures based on the muscle signals recorded at the forearm level. I was in charge of developing a recognition engine that can run on an ARM micro-controller and can address a large number of users. I invented what we called a muscle language and used NLP techniques to design a gesture recognition engine. The algorithm had to run on a low computational power processor and still works well for our users. It was a very successful project.
You do not need to be innovative, anymore. The innovation happens in research labs of big companies, or very few startups. They introduce powerful libraries, and you just need to learn how to use and tune those libraries. So, you do not need to be an innovative problem solver. Especially, when you have no constraint on computation power or data storage.
#personal-growth #personal-development #artificial-intelligence #data-science #data analysis
If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.
If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.
In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.
#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition
Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
In this article, we list down 50 latest job openings in data science that opened just last week.
(The jobs are sorted according to the years of experience r
Skills Required: Real-time anomaly detection solutions, NLP, text analytics, log analysis, cloud migration, AI planning, etc.
Skills Required: Data mining experience in Python, R, H2O and/or SAS, cross-functional, highly complex data science projects, SQL or SQL-like tools, among others.
Skills Required: Data modelling, database architecture, database design, database programming such as SQL, Python, etc., forecasting algorithms, cloud platforms, designing and developing ETL and ELT processes, etc.
Skills Required: SQL and querying relational databases, statistical programming language (SAS, R, Python), data visualisation tool (Tableau, Qlikview), project management, etc.
**Location: **Bibinagar, Telangana
Skills Required: Data science frameworks Jupyter notebook, AWS Sagemaker, querying databases and using statistical computer languages: R, Python, SLQ, statistical and data mining techniques, distributed data/computing tools such as Map/Reduce, Flume, Drill, Hadoop, Hive, Spark, Gurobi, MySQL, among others.
#careers #data science #data science career #data science jobs #data science news #data scientist #data scientists #data scientists india
Want to know how to become a data scientist from scratch? This comprehensive guide will take you through every necessary step to become a successful data scientist.
A Complete Career Guide on How to Become a Data Scientist
Data science has become the hottest career option for students. It’s become one of the fastest-growing career paths. In this high-tech world, every business and organization needs data scientists to leverage their data to the fullest extent. This provides ongoing opportunities for those who want to get hired into a data scientist role. This blog post will take you through all the necessary steps you need to know to become a successful data scientist.
#learn-data-science #data-science #data-science-skills #become-a-data-scientist #data-scientist
The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.
This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.
As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).
This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.
#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management
Around once a month, I get emailed by a student of some type asking how to get into Data Science, I’ve answered it enough that I decided to write it out here so I can link people to it. So if you’re one of those students, welcome!
I’ll segment this into basic advice, which can be found quite easily if you just google ‘how to get into data science’ and advice that is less common, but advice that I’ve found very useful over the years. I’ll start with the latter, and move on to basic advice. Obviously take this with a grain of salt as all advice comes with a bit of survivorship bias.
#big data & cloud #data science #data scientist #statistics #aspiring data scientist #advice for aspiring data scientists